Sarus Suite: Cloud-native Containers for HPC

2026-04-18

AI Summary

This work addresses the pressing need in high-performance computing (HPC) systems to simultaneously support rapidly evolving AI/ML software, seamless scheduler integration, scalable launch capabilities, and production-grade performance—challenges that existing container solutions often fail to meet due to reliance on specialized runtimes incompatible with mainstream cloud-native ecosystems. The authors propose a cloud-native container architecture tailored for HPC, built atop an unmodified upstream Podman engine. By decoupling HPC-specific functionality into an integration layer through declarative runtime configuration, native scheduler execution, distributed shared image access, and standardized host capability injection, the design maintains full compatibility with the Open Container Initiative (OCI) ecosystem while supporting Kubernetes manifests and standard images such as those from NGC. Evaluations on a Cray EX GH200 system demonstrate that workloads including PyFR, SPH-EXA, Megatron-LM, and Pynamic achieve performance and scalability on par with Enroot+Pyxis, alongside significantly faster single-node container startup times.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Other Foundations of Human Computation & AI
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

High-performance computing (HPC) systems must support fast-moving software stacks, especially in AI/ML, while preserving scheduler control, scalable startup, and production performance. Yet many HPC container solutions rely on specialized runtime stacks that weaken continuity with mainstream cloud-native workflows and require ongoing effort to sustain compatibility with the evolving upstream ecosystem. We argue that HPC should specialize the integration layer while keeping the container engine aligned with upstream container evolution. We present Sarus Suite, an upstream-aligned HPC container architecture built around an unchanged Podman engine. Sarus Suite adds the HPC-specific functionality needed for production use through complementary system layers for declarative runtime specification, scheduler-native execution, scalable shared-image access, and standards-based host capability injection. We evaluate Sarus Suite on a Cray EX GH200 system using communication-intensive HPC workloads, large scale AI training, metadata-heavy startup workloads, and container startup measurements. Across PyFR, SPH-EXA, Megatron-LM, and Pynamic, Sarus Suite matches the performance and scaling of the production Enroot+Pyxis baseline while delivering consistently faster per-node container startup. The architecture also enables direct use of upstream OCI images, including NGC-based images, and supports cloud-native multi-container workflows expressed through Kubernetes manifests. These results show that HPC-grade containers do not require an HPC-specific runtime, provided that scheduler semantics, scalable image access, and host integration are implemented in explicit system layers. This preserves upstream continuity and software agility while maintaining scheduler control, scalability, and production performance.

Problem

• HPC
• cloud-native
• containers
• upstream compatibility
• scheduler control

Innovation

• cloud-native containers
• HPC integration
• upstream-aligned architecture
• scheduler-native execution
• scalable image access